{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import glob, re, sys\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "results = []\n",
    "for fname in glob.glob('iteration*/all_affinity*.summary'):\n",
    "    \n",
    "    name = re.sub('\\.summary','', fname)\n",
    "    name = re.sub('/all_affinity_','-',name)\n",
    "    for line in open(fname):\n",
    "        vals = line.split()\n",
    "        #testset, iteration, rmse, R, S\n",
    "        if len(vals) == 5:\n",
    "            vals += [0,0,0]\n",
    "        for i in xrange(2,8):\n",
    "            vals[i] = float(vals[i])\n",
    "\n",
    "        results.append([name,name.split('-')[0]]+vals)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "results = pd.DataFrame(results,columns=('name','I','testset','iteration','rmse','R','S','aucpose','aucaff','top'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "results = results[((results.testset == 'all_affinity') | (results.testset == 'all_pose')) & (results.aucpose != 0) & (results.I != 'iteration6') ]\n",
    "results100 = results[results.iteration == '100k']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>name</th>\n",
       "      <th>I</th>\n",
       "      <th>testset</th>\n",
       "      <th>iteration</th>\n",
       "      <th>rmse</th>\n",
       "      <th>R</th>\n",
       "      <th>S</th>\n",
       "      <th>aucpose</th>\n",
       "      <th>aucaff</th>\n",
       "      <th>top</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1605</th>\n",
       "      <td>iteration2-g1_p0_h0</td>\n",
       "      <td>iteration2</td>\n",
       "      <td>all_affinity</td>\n",
       "      <td>100k</td>\n",
       "      <td>1.650597</td>\n",
       "      <td>0.575963</td>\n",
       "      <td>0.579083</td>\n",
       "      <td>0.919870</td>\n",
       "      <td>0.771714</td>\n",
       "      <td>0.561282</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2417</th>\n",
       "      <td>iteration5-32-32-32_7_1_1</td>\n",
       "      <td>iteration5</td>\n",
       "      <td>all_affinity</td>\n",
       "      <td>100k</td>\n",
       "      <td>1.662631</td>\n",
       "      <td>0.565374</td>\n",
       "      <td>0.560998</td>\n",
       "      <td>0.887932</td>\n",
       "      <td>0.746926</td>\n",
       "      <td>0.491492</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2877</th>\n",
       "      <td>iteration4-3_32_0_7</td>\n",
       "      <td>iteration4</td>\n",
       "      <td>all_affinity</td>\n",
       "      <td>100k</td>\n",
       "      <td>1.662631</td>\n",
       "      <td>0.565374</td>\n",
       "      <td>0.560998</td>\n",
       "      <td>0.887932</td>\n",
       "      <td>0.746926</td>\n",
       "      <td>0.491492</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1477</th>\n",
       "      <td>iteration2-g2_p0_h0</td>\n",
       "      <td>iteration2</td>\n",
       "      <td>all_affinity</td>\n",
       "      <td>100k</td>\n",
       "      <td>1.668115</td>\n",
       "      <td>0.567108</td>\n",
       "      <td>0.558524</td>\n",
       "      <td>0.915495</td>\n",
       "      <td>0.749532</td>\n",
       "      <td>0.551665</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1637</th>\n",
       "      <td>iteration2-g1_p1_h0</td>\n",
       "      <td>iteration2</td>\n",
       "      <td>all_affinity</td>\n",
       "      <td>100k</td>\n",
       "      <td>1.670774</td>\n",
       "      <td>0.568650</td>\n",
       "      <td>0.571575</td>\n",
       "      <td>0.920238</td>\n",
       "      <td>0.805854</td>\n",
       "      <td>0.595314</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                           name           I       testset iteration      rmse  \\\n",
       "1605        iteration2-g1_p0_h0  iteration2  all_affinity      100k  1.650597   \n",
       "2417  iteration5-32-32-32_7_1_1  iteration5  all_affinity      100k  1.662631   \n",
       "2877        iteration4-3_32_0_7  iteration4  all_affinity      100k  1.662631   \n",
       "1477        iteration2-g2_p0_h0  iteration2  all_affinity      100k  1.668115   \n",
       "1637        iteration2-g1_p1_h0  iteration2  all_affinity      100k  1.670774   \n",
       "\n",
       "             R         S   aucpose    aucaff       top  \n",
       "1605  0.575963  0.579083  0.919870  0.771714  0.561282  \n",
       "2417  0.565374  0.560998  0.887932  0.746926  0.491492  \n",
       "2877  0.565374  0.560998  0.887932  0.746926  0.491492  \n",
       "1477  0.567108  0.558524  0.915495  0.749532  0.551665  \n",
       "1637  0.568650  0.571575  0.920238  0.805854  0.595314  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "results100.sort_values(by='rmse')[:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>name</th>\n",
       "      <th>I</th>\n",
       "      <th>testset</th>\n",
       "      <th>iteration</th>\n",
       "      <th>rmse</th>\n",
       "      <th>R</th>\n",
       "      <th>S</th>\n",
       "      <th>aucpose</th>\n",
       "      <th>aucaff</th>\n",
       "      <th>top</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2577</th>\n",
       "      <td>iteration1-p1_rec0_astrat0_b1</td>\n",
       "      <td>iteration1</td>\n",
       "      <td>all_affinity</td>\n",
       "      <td>100k</td>\n",
       "      <td>1.732900</td>\n",
       "      <td>0.532962</td>\n",
       "      <td>0.550466</td>\n",
       "      <td>0.925966</td>\n",
       "      <td>0.785106</td>\n",
       "      <td>0.528977</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2576</th>\n",
       "      <td>iteration1-p1_rec0_astrat0_b1</td>\n",
       "      <td>iteration1</td>\n",
       "      <td>all_pose</td>\n",
       "      <td>100k</td>\n",
       "      <td>1.749490</td>\n",
       "      <td>0.524212</td>\n",
       "      <td>0.540142</td>\n",
       "      <td>0.925966</td>\n",
       "      <td>0.785106</td>\n",
       "      <td>0.634279</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1777</th>\n",
       "      <td>iteration2-g2_p2_h0</td>\n",
       "      <td>iteration2</td>\n",
       "      <td>all_affinity</td>\n",
       "      <td>100k</td>\n",
       "      <td>1.704713</td>\n",
       "      <td>0.558476</td>\n",
       "      <td>0.557435</td>\n",
       "      <td>0.922099</td>\n",
       "      <td>0.823510</td>\n",
       "      <td>0.610851</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1776</th>\n",
       "      <td>iteration2-g2_p2_h0</td>\n",
       "      <td>iteration2</td>\n",
       "      <td>all_pose</td>\n",
       "      <td>100k</td>\n",
       "      <td>1.738621</td>\n",
       "      <td>0.546849</td>\n",
       "      <td>0.546129</td>\n",
       "      <td>0.922099</td>\n",
       "      <td>0.823510</td>\n",
       "      <td>0.618002</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>193</th>\n",
       "      <td>iteration3-none_relu_lr0.010</td>\n",
       "      <td>iteration3</td>\n",
       "      <td>all_affinity</td>\n",
       "      <td>100k</td>\n",
       "      <td>1.681941</td>\n",
       "      <td>0.550760</td>\n",
       "      <td>0.550246</td>\n",
       "      <td>0.921432</td>\n",
       "      <td>0.776856</td>\n",
       "      <td>0.567941</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                               name           I       testset iteration  \\\n",
       "2577  iteration1-p1_rec0_astrat0_b1  iteration1  all_affinity      100k   \n",
       "2576  iteration1-p1_rec0_astrat0_b1  iteration1      all_pose      100k   \n",
       "1777            iteration2-g2_p2_h0  iteration2  all_affinity      100k   \n",
       "1776            iteration2-g2_p2_h0  iteration2      all_pose      100k   \n",
       "193    iteration3-none_relu_lr0.010  iteration3  all_affinity      100k   \n",
       "\n",
       "          rmse         R         S   aucpose    aucaff       top  \n",
       "2577  1.732900  0.532962  0.550466  0.925966  0.785106  0.528977  \n",
       "2576  1.749490  0.524212  0.540142  0.925966  0.785106  0.634279  \n",
       "1777  1.704713  0.558476  0.557435  0.922099  0.823510  0.610851  \n",
       "1776  1.738621  0.546849  0.546129  0.922099  0.823510  0.618002  \n",
       "193   1.681941  0.550760  0.550246  0.921432  0.776856  0.567941  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "results100.sort_values(by='aucpose',ascending=False)[:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>name</th>\n",
       "      <th>I</th>\n",
       "      <th>testset</th>\n",
       "      <th>iteration</th>\n",
       "      <th>rmse</th>\n",
       "      <th>R</th>\n",
       "      <th>S</th>\n",
       "      <th>aucpose</th>\n",
       "      <th>aucaff</th>\n",
       "      <th>top</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>68</th>\n",
       "      <td>iteration3-batch_relu_lr0.010</td>\n",
       "      <td>iteration3</td>\n",
       "      <td>all_pose</td>\n",
       "      <td>100k</td>\n",
       "      <td>1.693488</td>\n",
       "      <td>0.553803</td>\n",
       "      <td>0.551250</td>\n",
       "      <td>0.916430</td>\n",
       "      <td>0.762815</td>\n",
       "      <td>0.656720</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>iteration3-batch_leaky_lr0.010</td>\n",
       "      <td>iteration3</td>\n",
       "      <td>all_pose</td>\n",
       "      <td>100k</td>\n",
       "      <td>1.702117</td>\n",
       "      <td>0.546954</td>\n",
       "      <td>0.535193</td>\n",
       "      <td>0.914155</td>\n",
       "      <td>0.761463</td>\n",
       "      <td>0.651788</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>636</th>\n",
       "      <td>iteration4.6-0.010_1_Adam_0.010</td>\n",
       "      <td>iteration4.6</td>\n",
       "      <td>all_pose</td>\n",
       "      <td>100k</td>\n",
       "      <td>1.749769</td>\n",
       "      <td>0.531438</td>\n",
       "      <td>0.534019</td>\n",
       "      <td>0.917711</td>\n",
       "      <td>0.770594</td>\n",
       "      <td>0.645869</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>452</th>\n",
       "      <td>iteration3-batch_tanh_lr0.010</td>\n",
       "      <td>iteration3</td>\n",
       "      <td>all_pose</td>\n",
       "      <td>100k</td>\n",
       "      <td>1.741043</td>\n",
       "      <td>0.512958</td>\n",
       "      <td>0.504249</td>\n",
       "      <td>0.887398</td>\n",
       "      <td>0.761664</td>\n",
       "      <td>0.641184</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2400</th>\n",
       "      <td>iteration5-64-32-16_3_1_1</td>\n",
       "      <td>iteration5</td>\n",
       "      <td>all_pose</td>\n",
       "      <td>100k</td>\n",
       "      <td>1.721907</td>\n",
       "      <td>0.528304</td>\n",
       "      <td>0.516657</td>\n",
       "      <td>0.910429</td>\n",
       "      <td>0.742457</td>\n",
       "      <td>0.640937</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                 name             I   testset iteration  \\\n",
       "68      iteration3-batch_relu_lr0.010    iteration3  all_pose      100k   \n",
       "20     iteration3-batch_leaky_lr0.010    iteration3  all_pose      100k   \n",
       "636   iteration4.6-0.010_1_Adam_0.010  iteration4.6  all_pose      100k   \n",
       "452     iteration3-batch_tanh_lr0.010    iteration3  all_pose      100k   \n",
       "2400        iteration5-64-32-16_3_1_1    iteration5  all_pose      100k   \n",
       "\n",
       "          rmse         R         S   aucpose    aucaff       top  \n",
       "68    1.693488  0.553803  0.551250  0.916430  0.762815  0.656720  \n",
       "20    1.702117  0.546954  0.535193  0.914155  0.761463  0.651788  \n",
       "636   1.749769  0.531438  0.534019  0.917711  0.770594  0.645869  \n",
       "452   1.741043  0.512958  0.504249  0.887398  0.761664  0.641184  \n",
       "2400  1.721907  0.528304  0.516657  0.910429  0.742457  0.640937  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "results100.sort_values(by='top',ascending=False)[:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.JointGrid at 0x7f6830bb8fd0>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f6830afd0d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.jointplot(x='rmse',y='R',data=results100,alpha=.5,xlim=(1.5,2.5),ylim=(.25,.6))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.JointGrid at 0x7f682e9d28d0>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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VTMSUTMTU2z/5ayPuc9dxc47SqW+aqf995dIjmBBThRIDELwNPSn9cUdaL23ZpXR2cu8MS9RWaV5dXGcvnK2e1MARToipEmmJmVmrpK9KqpZ0l7t/cdTjKyXdIal7aNOd7n5XUUMCKGmdPSm90NuvLf17Jl1gNSa9KRnXzoFMWU3mCHXG4UREVmJmVi1ptaSLJXVJWmdma9x9w6hd73f3W4oeEEDJ6+xJ6e/WbNC2XXu1e+/k59Q3Hp3QUbU1MjMmcwQmyokdZ0va6O4vufteSfdJuiLCPAACsn8yx7ZdezQ4OHjIyRz776BSbVJdbbViVaZEjem8E47WrKNq1ZfOqpnJHMGJ8nBio6TNeetdks4ZY78VZrZc0vOSPuHum0fvYGarJK2SpAULyn/4DOBPkzkSsWqlM+NXWJWkN81K6I09Wbkkl2v+zIROPrZOR02LBXk1jvzXvbnHNkacJjqlPrHj3yX9q7vvMbMPS7pH0oWjd3L3NkltktTS0nK41/sEEIDuvrR6dryhF17v16EOJE6fVq3F9TP0qdaTgiqq8eS/7p2wZGnFvu5FWWLdkubnrTfpTxM4JEnuvi1v9S5JXy5CLgBFtv+Nyt196YJHRbsHMnry5R3yQ7x8V5k0e3qt3pSMl02BFereJ18t+8kdUZ4TWydpsZktNLNaSddJWpO/g5k15K1eLqmziPkAFEH+G5UbknGl0hm1rd2kzp7UuJ/3/Ou7lB2U9o1TYrVV0tGJmOpnxrVnvB0RrMhKzN2zkm6R9Ihy5fQDd3/GzG43s8uHdvu4mT1jZr+X9HFJK6NJC2Cq5L9RucpseLm9o3fcz/tjX3rcx6fVSHPr4pJJ297YWzbT5jFSpOfE3P1hSQ+P2vb5vOXPSPpMsXMBKJ7uvrQakvER2+riNeoep6Qeerpb6UPeLMyU3TcokylWXaXW5vojkBalhmsnAohU46yE+gdGXrD3UG84/uZjLw1PmT+Y6irT7syg6uI1+tiFb66482GVghIDEKnW5nql0hml0hkNug8vjzdy2rTtDQ3dbPkA+8stVl2l5Yvn6BvvOUOXLq3cKejlrtSn2AMoc0saklq1fOGI2YnXntV00JFTZ09Ke7ODMjPVVLlG38C5psrUctwsndSQVCqdKcJ3UDrKfSbiWCgxAJGYzLR6KTcRpH7mNL22c49i1SZTrshcudmI71hyjE48duaI/TmUWL4oMQBFt39afTIRGzGtPv+STwcrue6+tM5bNFe/eO517RzIqqrKdFSNtDc7qCvPbFL9zD+dSzvUBBGEjxIDUHT50+olDf93/6hpvJJrnJVQKp3Rn51cr41b3tCugaxi1absoCseG/mSVk5XpMfYmNgBoOi6+9Kqi48snPxR03jvHds/EaS2plrnLJytsxfO1gnzZuivLjhhwhNEED5GYgCKbv9oav8ITBo5ajrYe8c2DF3Fo38go+6+tJKJGp3SkByeCHLCvBkFTxApJ7On11bkpA6JEgMQgdbmerWt3SQpV079A9kR9/Eaq+Re3faGNm9Lq3HWUVrSMHP4c/InhCxpSFZEaeFPOJwIoOj2T6tPJmLqSQ0omYiNmNQx1nvHnuvdpRPrZ0z48lQob4zEAERivFHTWO8dazo6oePmTh+xH7MPQYkBKEmjS+4ff/r8uOfRUJk4nAggCJO5PBXKHyUGIAiHOo+GysThRADBYPYhRmMkBgAIFiUGAAgWJQYACBYlBgAIFiUGAAgWJQYACBYlBgAIFiUGAAgWJQYACBYlBgAIFiUGAAgWJQYACBYlBgAIFiUGAAgWJQYACBYlBgAIFjfFBDBCZ09K7R296u5Lq3FWQq3N9dyIEiUr0pGYmbWa2XNmttHMbhtnvxVm5mbWUsx8QKXp7Empbe0mpdIZNSTjSqUzalu7SZ09qaijAWOKrMTMrFrSaknvlHSKpOvN7JQx9quT9NeSnixuQqDytHf0KpmIKZmIqcpseLm9ozfqaMCYohyJnS1po7u/5O57Jd0n6Yox9vuCpC9JGihmOKASdfelVRcfeZahLl6j7r50RImA8UV5TqxR0ua89S5J5+TvYGZnSJrv7g+Z2ScP9kRmtkrSKklasGDBFEQFKsO0atPa57do775BzYzHtOiY6YpVV6txViLqaBiF172ckp2daGZVkv5B0t8cal93b3P3FndvmTdv3tSHA8pQZ09Kf0wNaNdAVrVVpvTerJ54cbs2b9+t1ub6qONhFF73cqIssW5J8/PWm4a27VcnqVnSY2b2sqS3SlrD5A5garR39Oq4OdN1zgmzNa22RtnB3KHE+pnTmJ2IkhXl4cR1khab2ULlyus6STfsf9DdU5Lm7l83s8ck/a27ry9yTqAidPel1ZCMq8pimlcXlyQNuqsnxelolK7IRmLunpV0i6RHJHVK+oG7P2Nmt5vZ5VHlAipV46yE+geyI7b1D2Q5H4aSFumbnd39YUkPj9r2+YPse0ExMgGVqrW5Xm1rN0nKHUbsH8gqlc7o2rOaIk4GHFzJTuwAUFxLGpJatXyhkomYelIDSiZiWrV8IefDUNK47BSAYUsakpQWgsJIDAAQLEoMABAsSgwAECxKDAAQLEoMABAsSgwAECxKDAAQLEoMABAsSgwAECxKDAAQLEoMABAsSgwAECxKDAAQLEoMABAsSgwAECxKDAAQLEoMABAsSgwAECxKDAAQLEoMABAsSgwAEKyaqAMAUersSam9o1fdfWk1zkqotbleSxqSUccCUCBGYqhYnT0pta3dpFQ6o4ZkXKl0Rm1rN6mzJxV1NAAFosRQsdo7epVMxJRMxFRlNrzc3tEbdTQABaLEULG6+9Kqi488ol4Xr1F3XzqiRAAmihJDxWqclVD/QHbEtv6BrBpnJSJKBGCiKDFUrNbmeqXSGaXSGQ26Dy+3NtdHHQ1AgZidiLJ1qJmHSxqSWrV84Yh9rj2ridmJQEAoMZSl/TMPk4nYiJmHq5YvPKDIKC0gXBxORFli5iFQGSIdiZlZq6SvSqqWdJe7f3HU4x+R9FFJ+yTtkrTK3TcUPSiC092XVkMyPmJbqc885I3XwMRFNhIzs2pJqyW9U9Ipkq43s1NG7Xavu5/m7qdL+rKkfyhyTAQqtJmHDz3drU/c93s99HSPXt32hjZt2cUbr1Gw7W/sjTpCZKI8nHi2pI3u/pK775V0n6Qr8ndw9515q9MleRHzIWAhzTzs7Enp6//xomTS7Okx7ckO6vnXd2nfvkEOfwKHEGWJNUranLfeNbRtBDP7qJm9qNxI7ONjPZGZrTKz9Wa2fsuWLVMSFmHZP/MwmYipJzWgZCJ2wKSOUtHe0avMvkHNjNfIzBSPVWtaTZVe2zlQ0oc/Ea38173+vu1Rx4lMyc9OdPfVklab2Q2SPifppjH2aZPUJkktLS2M1iApnJmH3X1pzZleqz3ZQcVj1ZKkaTVV2v5GRue+uTQPfyJ6+a97JyxZWrGve1GOxLolzc9bbxradjD3SXr3lCYCItA4K6GGZFx7soMayOyTu2vnQFY1VVaShz+BUhJlia2TtNjMFppZraTrJK3J38HMFuetXirphSLmA4qitbleVVVVOql+hqbVVGnb0En6j73jzUGMJBG92dNro44QmcgOJ7p71sxukfSIclPsv+Puz5jZ7ZLWu/saSbeY2UWSMpJ2aIxDiUDo8q8cEqup1rlvnsv0eqBAkZ4Tc/eHJT08atvn85b/uuihgAiEcv4OKDVcsQMAECxKDAAQLEoMABAsSgwAAlfJl50q+Tc7AwfDBXMBUGIIUqH3C9u/L2UHlCcOJyJIhd4vbH/ZpdKZEWXH1eGB8kCJIUjdfWnVxUceSBjrfmHcHBMob5QYglTo/cIKLTsgZJV82SlKDEEq9H5hod0cE8DEUGIIUqH3Cwvp5pgAJo7ZiQhWIdcbzL+47v7Zidee1cTsRKBMUGIoe1xcFyhfHE4EAASLEgMABIsSAwAEixIDAASLEgMABGtSJWZmVWZ245EOAwDARIxbYmY208w+Y2Z3mtl/t5yPSXpJ0jXFiQgAwNgO9T6x70raIekJSR+U9D8kmaR3u/tTU5wNAIBxHarETnD30yTJzO6S1CNpgbsPTHkyAAAO4VDnxDL7F9x9n6QuCgwAUCoONRJbZmY7h5ZNUmJo3SS5u8+c0nQAAIxj3BJz9+piBQEOpbMnNeJCvq3N9VwTEahwvE8MQejsSalt7Sal0hk1JONKpTNqW7tJnT2pqKMBkdv+xt6oI0SGq9ijpBxstNXe0atkIqZkIiZJw/9t7+hlNAZUMEZiKBnjjba6+9Kqi4/8m6suXqPuvnREaQGUAkoMJSN/tFVlNrzc3tGrxlkJ9Q9kR+zfP5BV46xERGkBlAJKDCVjvNFWa3O9UumMUumMBt2Hl1ub6yNKC6AUUGIoGeONtpY0JLVq+UIlEzH1pAaUTMS0avlCzocBkmZPr406QmSY2IGS0dpcr7a1myTlRmD9A1ml0hlde1aTJGlJQ5LSAjBCpCMxM2s1s+fMbKOZ3TbG47ea2QYze9rMHjWz46LIieJgtAVgoiIbiZlZtaTVki6W1CVpnZmtcfcNebv9l6QWd99tZjdL+rKka4ufFsXCaAvAREQ5Ejtb0kZ3f8nd90q6T9IV+Tu4+8/dfffQ6m8kNRU5IwCghEVZYo2SNuetdw1tO5gPSPrJWA+Y2SozW29m67ds2XIEIwJAaeJ1LyeI2Ylm9h5JLZLuGOtxd29z9xZ3b5k3b15xwwFABHjdy4lydmK3pPl5601D20Yws4skfVbS+e6+p0jZAAABiHIktk7SYjNbaGa1kq6TtCZ/BzN7i6RvSbrc3V+PICMAoIRFVmLunpV0i6RHJHVK+oG7P2Nmt5vZ5UO73SFphqQfmtlTZrbmIE8HAKhAkb7Z2d0flvTwqG2fz1u+qOihAADBCGJiBwAAY6HEAADBosQAAMGixAAAwaLEAADBosQAAMGixAAAwaLEAADBosQAAMGixAAAwaLEAADBosQAAMGixAAAwaLEAADBosQAAMGixAAAwaLEAADBosQAAMGixAAAwaLEAADBosQAAMGixAAAwaLEAADBosQAAMGixAAAwaLEAADBosQAAMGixAAAwaLEAADBosQAAMGixAAAwaLEAADBirTEzKzVzJ4zs41mdtsYjy83s9+ZWdbMrooiIwCgdEVWYmZWLWm1pHdKOkXS9WZ2yqjdXpW0UtK9xU0HAAhBTYRf+2xJG939JUkys/skXSFpw/4d3P3loccGowgIAChtUR5ObJS0OW+9a2jbhJnZKjNbb2brt2zZckTCAUAp43Uvpywmdrh7m7u3uHvLvHnzoo4DAFOO172cKEusW9L8vPWmoW0AABQkyhJbJ2mxmS00s1pJ10laE2EeAEBgIisxd89KukXSI5I6Jf3A3Z8xs9vN7HJJMrOzzKxL0tWSvmVmz0SVFwBQeqKcnSh3f1jSw6O2fT5veZ1yhxkBADhAWUzsAABUJkoMABAsSgwAECxKDAAQLEoMABAsSgwAECxKDAAQLEoMABAsSgwAECxKDAAQLEoMABAsSgwAECxKDAAQLEoMABAsSgwAEKxI7yeG6HX2pNTe0avuvrQaZyXU2lyvJQ3JqGMBQEEYiVWwzp6U2tZuUiqdUUMyrlQ6o7a1m9TZk4o6GgAUhBKrYO0dvUomYkomYqoyG15u7+iNOhoAFIQSq2DdfWnVxUceUa6L16i7Lx1RIgCYGEqsgjXOSqh/IDtiW/9AVo2zEhElAoCJocQqWGtzvVLpjFLpjAbdh5dbm+ujjgYABaHEKtiShqRWLV+oZCKmntSAkomYVi1fyOxEAMFgin2FW9KQpLQABIuRGAAgWJQYACBYlBgAIFiUGAAgWJQYACBYlBgAIFiUGAAgWLxPrMxwaxUAlYSRWBnh1ioAKk2kJWZmrWb2nJltNLPbxnh8mpndP/T4k2Z2fPFThoNbqwCoNJGVmJlVS1ot6Z2STpF0vZmdMmq3D0ja4e6LJP2jpC8VN2VYuLUKgEoT5UjsbEkb3f0ld98r6T5JV4za5wpJ9wwtPyDpHWZmRcwYFG6tAqDSRFlijZI25613DW0bcx93z0pKSZoz+onMbJWZrTez9Vu2bJl8Mo3fAAAKfklEQVSiuKWPW6sAlYPXvZyymNjh7m3u3uLuLfPmzYs6TmS4tQpQOXjdy4lyin23pPl5601D28bap8vMaiQlJW0rTrwwcWsVAJUkypHYOkmLzWyhmdVKuk7SmlH7rJF009DyVZL+w929iBkBACUsspGYu2fN7BZJj0iqlvQdd3/GzG6XtN7d10j6tqTvmtlGSduVKzoAACRFfMUOd39Y0sOjtn0+b3lA0tXFzgUACENZTOwAAFQmSgwAECxKDAAQLEoMABAsSgwAECxKDAAQLEoMABAsSgwAECxKDAAQLEoMABAsSgwAECxKDAAQLEoMABAsSgwAECxKDAAQLEoMABAsSgwAECxKDAAQLEoMABAsSgwAECxKDAAQLEoMABAsSgwAECxKDAACt/2NvVFHiAwlBgAIVk3UAcpdZ09K7R296u5Lq3FWQq3N9ZJ0wLYlDcmIkwJAeCixCejsSenrj76g/3x5h9J7s0rUVmvRMXU6Z+Gc4SLq7Enpu0+8oide2qa+NzIayO5ToqZK8ViVsi597zevqG5atZbNn6UFc6Yrlc6obe0mrVq+kCIDgAmixA6hsyel7z3xih7+Q492pLMjHktnBvWfL21X5x936q5fvqT03n0aHOM50plBKZ1brqmS9g3W6LneXZoRr9HcGXFJ0veeeEVz6+KMzgBgAiixcXT2pPSVR57X717ZfkCBSdKg5/7bP5Ads7zGkh2U+tJZzasb1MbX39DcGXHtyWb1q43bdeHJx6ghGWd0BmDC7n3y1Sl9/hvOWTClzz9ZTOwYR3tHr7bu2qPUGAWWr9ACy9e7c496d+aGZxv+2K+jj4opmYipykzJRG65vaN3Es8MAJWDEhtHd19aO9N7J1VSh7InO6i9WVcqndGO3Rktaagb8XhdvEbdfekp+MoAUD4iOZxoZrMl3S/peEkvS7rG3XeMsV+7pLdKetzdLytGtvzZhK9u362dhxiFTYZJcs8di0wmYnrbojmqrakesU//QFaNsxJH/GsDQDmJaiR2m6RH3X2xpEeH1sdyh6T3FitUZ09KbWs3KZXOqCEZ17F107Qrs++If526eI0Wzp2uq1vm6xMXn6j3nnucUumMUumMBt2Hl/dPxwcAjC2qErtC0j1Dy/dIevdYO7n7o5L6ixWqvaN3+HxUlZkWzpuhBUcnNL127B/Tu06t1+mNM1VbZQV/jYaZtZo3Y5relPeesSUNSa1avlDJREw9qQElEzEmdQBAAaKanVjv7j1Dy69JKokhR3dfWg3J+IhtZx53tPZkXWfNPUrP9fZr10BWtTXVWrX8eH34/MXDMxg3b9+tja/vOuD82dwZMR1bF5fMtHtvVtNi1XrL/Fl677nHjSipJQ1JSgvApMyeXluyswen2pSVmJn9TNKxYzz02fwVd3cz88P8WqskrZKkBQsm/w/ZOCuhVDqjZCI2vC0eq9HbFs3RvLq45tbFD3gP15KGpP72khO5AgeAojpSr3uhm7ISc/eLDvaYmfWaWYO795hZg6TXD/NrtUlqk6SWlpZJF2Jrc73a1m6SlDtv1T+QVSqdOeShPUZRAIrtSL3uhS6qc2JrJN00tHyTpB9FlGMEzk0BQFiiOif2RUk/MLMPSHpF0jWSZGYtkj7i7h8cWv+lpJMlzTCzLkkfcPdHpjIYoyoACEckJebu2yS9Y4zt6yV9MG/97cXMBQAIC1fsAAAEixIDAASLEgMABIsSAwAEixIDAASLEgMABIsSAwAEixIDAASLEgMABIsSAwAEixIDAASLEgMABMvcy+s2NGa2Rbkr40+luZK2TvHXmCyyTU6pZivVXBLZJqvQbFvdvbWQJzSz9kL3LTdlV2LFYGbr3b0l6hxjIdvklGq2Us0lkW2ySjlbiDicCAAIFiUGAAgWJTY5bVEHGAfZJqdUs5VqLolsk1XK2YLDOTEAQLAYiQEAgkWJAQCCRYkVwMxmm9lPzeyFof8efZD92s2sz8x+PMV5Ws3sOTPbaGa3jfH4NDO7f+jxJ83s+KnMM8Fsy83sd2aWNbOripWrwGy3mtkGM3vazB41s+NKKNtHzOwPZvaUmT1uZqeUSra8/VaYmZtZ0aaPF/BzW2lmW4Z+bk+Z2QdLJdvQPtcM/c49Y2b3FitbWXF3Pg7xIenLkm4bWr5N0pcOst87JP25pB9PYZZqSS9KOkFSraTfSzpl1D5/JembQ8vXSbq/SD+nQrIdL2mppP8n6aoi/hsWku3PJB01tHxzif3cZuYtXy6pvVSyDe1XJ2mtpN9IaimVbJJWSrqzWL9nE8y2WNJ/STp6aP2YYucshw9GYoW5QtI9Q8v3SHr3WDu5+6OS+qc4y9mSNrr7S+6+V9J9Q/ny5ed9QNI7zMymOFdB2dz9ZXd/WtJgEfJMNNvP3X330OpvJDWVULadeavTJRVrRlYhv2+S9AVJX5I0UKRcE8kWhUKyfUjSanffIUnu/nqRM5YFSqww9e7eM7T8mqT6CLM0Stqct941tG3Mfdw9KyklaU6JZIvKRLN9QNJPpjTRnxSUzcw+amYvKndk4OOlks3MzpA0390fKlKm/Qr9N10xdIj4ATObX5xoBWU7UdKJZvYrM/uNmVXkZaMOV03UAUqFmf1M0rFjPPTZ/BV3dzPjfQllzMzeI6lF0vlRZ8nn7qslrTazGyR9TtJNEUeSmVVJ+gflDtuVon+X9K/uvsfMPqzcEYoLI860X41yhxQvUG7Uv9bMTnP3vkhTBYYSG+LuFx3sMTPrNbMGd+8xswZJUQ77uyXl/zXZNLRtrH26zKxGUlLSthLJFpWCspnZRcr94XK+u+8ppWx57pP0f6c00Z8cKludpGZJjw0dsT5W0hozu9zd10ecTe6e/3t/l3Kj2GIo5N+0S9KT7p6RtMnMnleu1NYVJ2J54HBiYdboT3/13iTpRxFmWSdpsZktNLNa5SZurBm1T37eqyT9hw+dOS6BbFE5ZDYze4ukb0m6vMjnJwrJtjhv9VJJL5RCNndPuftcdz/e3Y9X7lxiMQrskNkkaeiPzv0ul9RZhFwFZZP0oHKjMJnZXOUOL75UpHzlI+qZJSF8KHc+6VHlXjh+Jmn20PYWSXfl7fdLSVskpZX7K+uSKcrzLknPKzf76bND225X7sVDkuKSfihpo6T/lHRCEX9Wh8p21tDP5g3lRofPlFC2n0nqlfTU0MeaEsr2VUnPDOX6uaRTSyXbqH0fU5FmJxb4c/s/Qz+33w/93E4uoWym3KHYDZL+IOm6YmUrpw8uOwUACBaHEwEAwaLEAADBosQAAMGixAAAwaLEAADBosSACTCzzw5dcfzpoauinxN1JqCSccUOoEBmdq6kyySd4bnLGM1V7grlACJCiQGFa5C01YcuR+XuWyPOA1Q83uwMFMjMZkh6XNJRyl3d4353/0W0qYDKxjkxoEDuvkvSmZJWKXd5sfvNbGWkoYAKx0gMmCQzu0rSTe7+51FnASoVIzGgQGZ20qiryZ8u6ZWo8gBgYgcwETMkfd3MZknKKneXgFXRRgIqG4cTAQDB4nAiACBYlBgAIFiUGAAgWJQYACBYlBgAIFiUGAAgWJQYACBY/x8Wdu3DZrBteQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f682e9d2190>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.jointplot(x='S',y='R',data=results100,alpha=.5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.JointGrid at 0x7f682ea44550>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f682ea44250>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.jointplot(x='rmse',y='aucpose',data=results100,alpha=.5,xlim=(1.5,2.5))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.JointGrid at 0x7f682e544710>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f682e5448d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.jointplot(x='rmse',y='top',data=results100,alpha=.5,xlim=(1.5,2.5))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.JointGrid at 0x7f682e265490>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f682e641610>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.jointplot(x='aucpose',y='top',data=results100,alpha=.5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "# thank you stack overflow\n",
    "\n",
    "def is_pareto_efficient_dumb(costs):\n",
    "    \"\"\"\n",
    "    :param costs: An (n_points, n_costs) array\n",
    "    :return: A (n_points, ) boolean array, indicating whether each point is Pareto efficient\n",
    "    \"\"\"\n",
    "    is_efficient = np.ones(costs.shape[0], dtype = bool)\n",
    "    for i, c in enumerate(costs):\n",
    "        is_efficient[i] = np.all(np.any(costs>=c, axis=1))\n",
    "    return is_efficient"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "costs = np.array(results100[['rmse','aucpose','top','R']])\n",
    "costs[:,1:] *= -1\n",
    "pareto = is_pareto_efficient_dumb(costs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7f682e08a350>]"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f682e1b4410>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "p = results100[pareto]\n",
    "plt.plot(p.rmse,p.aucpose,'o')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "r = results100[results100.testset == 'all_pose']\n",
    "costs = np.array(r[['top','S']])*-1\n",
    "pareto = is_pareto_efficient_dumb(costs)\n",
    "p = r[pareto]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "ra = results100[results100.testset == 'all_affinity']\n",
    "costs = np.array(ra[['top','S']])*-1\n",
    "paretoa = is_pareto_efficient_dumb(costs)\n",
    "pa = ra[paretoa]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.5, 0.6138508802164)"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f682e08a910>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(results100.top,results100.S,'o',alpha=.2)\n",
    "\n",
    "plt.plot(p.top,p.S,'o')\n",
    "plt.plot(pa.top,pa.S,'o',alpha=.5)\n",
    "plt.xlabel('Top')\n",
    "plt.ylabel('S')\n",
    "plt.xlim(.5)\n",
    "plt.ylim(.5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.5, 0.5930056183938)"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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i6w726iUqutv4+LibmJjodBiyzeqnkqg/Gak9YmtqvQfrSwTXkllmlnPsG4htKCkrmfubmT3mnBtv9jzNDSVdrb4BFVj5f34mpV+rW7BaiSAQgJ9+w74Nn+hVctgZOt0bSmRL/NbTbadQ7zFppJKFdDX1dNua+iois8oy51ipLnrzEXUCkQolC9mSTtdHq6db6xr31XBPhHPTyyQiIUIB4+krCziMUwcHVmY5qJUmOr2fvbJTP5cX1MAtm+Z143KrX2R94ZtbbV89fWWR4yM97OmPcWYqSaFUBoxw0LhlXz/pfJFIKLCSkBv38w1jvVyYTXH22hIO48SeXk4d6p7rcKtzRIUauH1kp57MvGxc3sgFc9SA2txq+6pULjOXyrGnP8Zyrkh/dbBdMlsEXh08t9q6y9kip5+YJBQ0BmJhwPHc5CLJTIG3nxhjMBHhwmyKR16eZXopy1hfjLfeOMLREe+nV2n1+6bOERujBm6P+WkSw3bzsnHZT/N57QSr7avhRIS5dGVf9UZD5IolcsUyAXOcmUryvXOzTC1mmVzIvGbduVSO6aUsg/EI8UiIeCTMQDzCQrrAU5cX+OrTk3zy62d48doSA/EwmXyRL//gMhdmvd1/G/m+qXPExihZeMzPJ72tTtvu5bw++iK312r7argnSjAA6XyR/QMxFjN5ppIZlrJFlrMFwgEY7Y1ydTHL9FLuunXn0gUiwQDR0KunkGgoSDJb4ImL8zx5cYHRvijxUIiLcxlCoSAD8TCPvDyLlzbyfdO8VBujZOExv5702lHi8XImV32R22u1fRUIwJ0n9xEJBSiWHScPDDAYDxMKGr2xMK/bP8Ce/hjHR3o4P7t83brBAOzpj5ErllfeI1cssZguMtwbJZUv0hMNEQkFiIaCzCxlyRXLfOP5KT71jRf50j9e9KSUsZHvm2Yi3hi1WXjMr10721Ff6+U0Kurl1F7r7av6dgTnYCgRwWr9aIGxvijZYmllzEV/PMydJ/fx1OUFLs9nGHCVNotktkAgAMdHenhlMUs6V6I3FiAcMq4uZLiazNIfDTEQD/PiK0s88vIMd75hH2+7cbRtbXgb+b5pGqCNUbLwmF9Pes1mA22VV43L+iK3Xyv7qv5ke3Uhw9NXFri2lGOkN8obDg5cN+5iIB7mqcsLK72hTh4YACAUDPDGg4P8/ZlXqveNy/NpQgE4sbePi3Np4uEgo30xnry0QDwcbFsPpI1+39Q5onXqOrsN/NgbarW5f2pdJds1EMuPn7td2vHZ/Lh9atWTyUyRR16eIRoKEAwaI71RCsUy773t0Lo9mup7sS2k8/zgwjzXlnIspPO89YYRMoUSxZIjEgpSdmVmlvP8izcdWOmi247t4cft6metdp1VstiltmOMxE7qw9440nkxU2BPX2zTn83P22chnef+b59jMVNgrDfCaF+MnkiIxXSOeCTE++840nT9xpP1Q89MkckXmVzM0hMNYRjL2SKOMjfv7eOHsymGeqIcH+lhrC/qq+2x07WaLNTAvUt5PfePn3uBbVRjZ4CLs2kuz2cold2mP5uft89gIkIiHOC2w4McHemlp1r67IuFmV7KrrvuWr/q33rjCIuZAqWyI1cospwtMpvOEgsHSeWKmBkhg4tzKZZzRV9tD6lQstikrXY79QMvL+/q115gm9F4Yi+WHQOxMJMLmZXnbPSz+X37jPXFWMpeH8tStsBYX2zNddbrYXd0pIf33naIg0Mxri5myZWKJMIB5lM5Ls1nCGD0xyPEwsGV7eqn7SFKFpuykwfatctO6vraeGLvjYYAx3Lu1c+30c/m9+1TKwkspnOUy2UW0zkWMwXeeuPImus0Ky0dHenhV99+I7/69hsIYFyYS5MrOYYSYVL5IvPpPNFQkOVcZQS5n7aHKFlsip+rEPxiJ/VhbzyxHxiMk8wWCAXZ9Gfz+/aplQTikRBXFzPEI6FVG7frS9g/uDhPsVS+7vHG0sFCOs/3z89ydCTBmw8PMxQPs5gpMpSIMLmQIZnJ0xMJ+m57iLrObkq7up3uZDup62tjd8xgwDg0lKA/Ht70Z+uG7XN0pKflnk9DiQjRUICnryxw6tAQfbHVr9N9fiZFqQzDvRFCoSAXZpYxHPliib5YmKJzJKqD+fy2PXY7JYtN8OtAO79Zrw97N3VvXO3EXpssb+uv67/PvNmJ+G4Y7eXJywucm17m1KHBVcc4JDMFhhNhcsUyPZEQR0d7mU5mmUpmuO3I8KauxCfbQ9VQm+D3KgS/68Y2Hy87A/jJVibi64uFK9fCKJXW7GFXuY5GlGyhRLZQIhEOMpQIc2JvvxKFz6lksQndUIXgZzttauhOlZK8eN+N7JvVStihYIA3Hxlec2Dn8dEeFtJ5jgwnKjPXLucIVueo0vfH35QsNsmvVQjdYCe1+WzkuhvtdGE2xcPPvUKpXGY4EaFYciyk81t+31b2TS1JTS5kuLqYfc1AuvWmsqn/oRUKGjft7fN1FaS8SslCtt1OavPpRClpIZ3n4eemCJkx3BsjVyxxcS7FkeGeDb9vY+nEjHX3TX1yPDyUIBYKcn52mWyxxIHBeEslbP3Q6k5KFrLt/Dq54mZ0opRU36PIzIiFK1/juVSOUNCarP2q1UpFyUwBx2unMqntm8bkuKc/Rm8stKU5xbqps8NupgZu2XZeTzXSLq2M0u/E4Lr6HkU10VCQuWr7WatWGy801hdjIB5ec9/UN2ovZQucmUry/GSSxy/ObaqDQjd2dtitVLKQjvB7VUSrbRGdKCX1x8MUS46Lc2kAoqEAyUyeYCCwoR55a5WKsoXSmqWEWnIslR1nppaIhYNEQkbZBTfVVrPTOjvsZCpZiKyi1VH6nSglHR/tIRCAI8MJQgGYXs5RdI47T+7d0PtuplRU6zZ+bma5eklVR65Y5oax3k3NYuD3ObLkVSpZiKxiI20R211KalePotVKRdNLWfrjYb515tqq7Qe19z43kyJAZWzF0ZEe+mJhnHNcmq+Udlptf9hJnR12OiUL2ZSd3ijp95NYOxJU43ghM0jliiykCxRKZcLBAFfm068ZrT6YiHDbkaGVi2fV2i4uz6dZTBd4+4mxla60zaqmdlJnh51O1VCyYdvdKNmJ6eB3yyj9WsJ40+FBzl5b4rEL81xZyBAOBggGKpdDferywmvWq22fa8ksL1xdZDlbIJktMNYf3dA1Kbqls4MoWcgmbOesu53qLbObTmK1bXxhNs1ob5SgGRdmU5Qc9MfCvHRt+TXr1LbPzHKOQhl6Y2HG+mLs649v+JoUu2UqlW6naigf6Zaqne0cW9DJ3jJ+77HVLrVtHA4GMTMioUqD88xSlj19MYzVL708mIiwbyDG6/f3Y2YwlSRXLBMNBUlWL5zkp6o72RqVLHyim/qb1/eiqdVXf+/cDFOL2bbHq94y3qtt4yPDcZZzRfLFMqGgsZAusJgtcNOevjXXrT8WDgzGyRZKG74mxU646uRuoGThE910QaXV6qvDwQCjvdG2f9n9fkW5naC2jW/a08fevhhlV5lnKhoKcGgozqlDg2uuW9+20xsNcWQ4cd01KWrjNdZKBt30I2m3UzWUT3TT5Hq1OuaHnpmiUIaRnjAHBuP0xcKk88Utz09UX/2m3jLeq23jRCTEmw4PcH42xdyyceuRIU4dGly3KrSxR9Vwb4Tbjx1eWWe1wY3ffWma/ngY52BqMctob1SD8rqAkoVP+L2rZqPX1FdXbTTBNRsprengvVe/jbOFEj9yYIDhnghzqTxPXlpo2n62XttOY5vTYqbA98/PYcAbDw5wdTFDKlcgHgmuXF3Prz+SdjslC5/oxl/Q7UhwrTRg75aG5k6q38btnHa9vsS8lC3w7RevkcwWyBddpTSZLRAJBZhcyHDLvtUvxSr+4GmbhZndZWZnzOysmX10lcfvNbNpM3ui+vfB6vJbzex7ZvasmT1lZu/3Mk4/6Maumu0Yi+BlA7YaTjenlsBLZceLryzx/NUkV+Yzq463aKa+zemla0tMLWYJWIDBRJiABSiWHFfm08ymcjt6PMtO4FnJwsyCwKeBO4HLwKNmdto591zDU7/knLuvYVka+CXn3EtmdgB4zMwecs5t/GjtIt32C7odVUReVb916qJEO0EyUyAUMJ68tMByrkix5AgG4MpCumkbRqP6EvPF2RSJSJBiqczQQJxIKMBIT5TZVJbBeETVjD7nZTXUHcBZ59w5ADP7IvAeoDFZvIZz7sW625Nmdg0YA3Z0suhGW01wXlW/aTbT621kDE9/PMyj52d5ZSlLXzRCT9RI5UpkSkWeurzAj9+8p+X3rf9BkSmUGOmJUCw7ggYOR75Uwgxdf7sLeFkNdRC4VHf/cnVZo/dVq5oeNLPDjQ+a2R1ABHjZmzClk7yqftP4jFe10j21vspuKVvg+aklIsEg4ZBRKDkcjmNDCc5eW9rw+9f28V1v2M+egTjHRnsJBmA+lSdbLPOOm/coUXSBTjdwfwX4gnMuZ2a/BjwAvKv2oJntB/4c+GXnXLlxZTP7EPAhgCNHjmxPxNJ2XlS/dVvvMi81K2WtVmVXKJaZnE9TNqM/FuKmsV7ikSCZ4mu+hi07dWiQxUyBxUyBIYsy1l+50NLbbhxty+cUb3mZLK4A9SWFQ9VlK5xzs3V3PwN8onbHzPqBvwV+yzn3yGpv4Jy7H7gfYHx8fPU5CWRX6sbeZV6oJII5AgTojYVWxsPUd09tTCalsiMRDbKQKnBibw9gvDy9jMNxeLiHb794DQDn2NC0NIOJCO84MdYVU9rIa3lZDfUocMLMjptZBLgHOF3/hGrJoeZu4Pnq8gjw18CfOece9DBG2aG6sXdZu9VKDJFg5Wp2hVLl6nZL2cJ1pazGKrvJhQw37+mjP1EZOJfMFJhL5XHAiT29PDe5yLOTSUIB2/CIa00a2L08K1k454pmdh/wEBAEPuuce9bMPg5MOOdOAx8xs7uBIjAH3Ftd/eeBHwdGzKy27F7n3BNexSs7T7f1Lmu3WonhhrFezkwliYWNaCjAuZllDg7GV0pZjVV2y7kSiUiQUwcHSERCvDCV5MaxXvriYZayRQbiEcC4upjlln39K++1m7f1buBpm4Vz7qvAVxuW/U7d7Y8BH1tlvc8Bn/MyNpGdrjYgzsy4ZV8/kwsZlrIFynDdr/rGKrtQEJLZAqcODdEXC7OcKxEJQiQUZDlXpL860jqZLa6soxHXO1+nG7hFxCP1JYa+WJhb9lXm7oqEAqteKrU2XubIcIJkpkAwYDjn6pJHL5MLGXLFEmD0RitVV9vZcaBbpvHfiZQsxHd0QmiPZo38623n2mONyWP/QIynryzgME4dHFgZcb0dHQc00LKzzLmd0YlofHzcTUxMdDoM2aL6E0L9CU4nhM1ZKyFsdDvXv05t3shab6japINeJ/faWJH67tC1klJtKnTZODN7zDk33ux5KlmIr2jkdXut1cjfynZupYRXn3RCAePZyUW+8+K1lqY336humsZ/J1KyEF/RCWF7NNvOF2ZTPPzcFKUyDCfCFEuVCyI1ljxqSWcxXeD752cplSEWMV6YSlIqu7aWCDXQsrOULMRXdELYHo3beSlb4NzMMrliGTN48tIifdEgw70RcsUyF+fSHBlOXDfq+/xMin94aZq+WIiXri2TCIfp7wmylCkw8cN50vky15K5ts37pIGWnaXLqoqvtGPac2mufjsnM3mevLzAcrbA6/b2cXE2zaW5FMFgADMjFg4SCweZS+VIZgrXzTW1tz/Gpbl0ZerxIGSLZaaSWeKRIOVSmYVMoW3Tw2ugZWcpWYiv6ISwPQYTEW4Y6+WHMym++tQkC6kCN4z10R+vzAo70hPl6kJm5fnRUIC5dKXtor694+BQgkyhTE84yHQyy3QyC8D+gTjJbGWW2XZeS14jwDtH1VDiO7t95PV2WEjnOTe9zLHRHjL5EpGQcWU+TW80RG80RLE3wvmZZbKFItFQkGQmTzBQKZE8eWlhpb2jMn6jj/PTKX44myIRDXJ4KEG5BMEAHBiMq81ph1CyENkl6ns3TS1mGe2NkoiE6I2FKJQcsbAxuZDhwGCcmeUsR0d6CQWM6eUswUCAO09W2h4a2ztu2tNHKl9i/2BEdfRVAAALQUlEQVSMZLbIQioHBDg0HOfFV5YJBeHIcKKzH162TMlCZBdoHND2wlSSVL5IPBLkwGCcM1NLREMBlrIFgoEEh4YS9McrEwnetLfvum6zjQ3NwYBxaCjOQHXuqHPTyxTLrjotiCOZLay0dWym2kiDNP1BbRYiu0B9O4OZMdITJUBlhtlaVVIZRxmIhAK8/cQYpw4N0h8Pk8wUOD+TWmmkXq1d6R0nxvjxm/fwz08d4E2HBxnrjZIvlYmEgpw6NMRYX2xT7RatXLhJtodKFiK7QOO4igODcV64mmc2lcc5RyZfIlsos38gBsBipsC56eU1p9ZYr13JucqFjqw21Btwzm2q3UKDNP1DJQuRXaDWzlDTFwsz2hdjMVPgoWen+M7Za4z1Rjk8lCBfLPPwc1OUy6yURGon7FZKB43vBZsfK6PL4/qHkoXILtA4fuVaMsv56WVuPzLEsZEEx4Z7mEvlWc4VSURClMowl8pd9xqtnqTbOVamnYlHtkbJQmQXaGxnmFnO8YaDg+zpj5HKl+iPR4iFg0xWx1YMJ8LMNVQbtXqSbudYGQ3S9A+1WYhvqRdMe9W3M3zrzLWVNozeaIhcsVQZT1G9oNFwT5TFbOXEvJmpNZqNlWl13zZea6M/HuaWfRqM1wkqWYgvqReMt+qrdw4MxskWSiQzeXoiAdL5IoEA3Hlyrycj6Te6bzVq2x+ULMSXGrt6bqSBVZqrr97pjYY4MtxD0YEDfjiTIp0vMZfKc3y0p+0nae3b7qRkIb6kXjDeCwaMZyYXmbgwSzhk3HlyL2N9UY6N9qz0ivKiNKd9253UZtGldnp9vqYq9079aO63HB1eaY+4MLs9Yxo2sm93+nHeTVSy6EK7oT5fvWC8s1Y10NlrS9vyi7/VfbsbjvNuomTRhXZDna+mKvfOWtVADtuWMQ2t7tvdcJx3E1VDdaHdculRTVXujbWqgU7s6SWdr3Sd9fpKdK3s291ynHcLJYsupPp82Yq1Lk/65iOVpFA/pmHfQG+1zWBh29sM6o/zpWyByYUMs6k8g/Hwpmewlc1TNVQXUn2+bMV61UD1YxqOj/Zwbnq5Y20GteP8WjLLC1NJlrMFwgEY7Y2q7aIDlCy6kOrzZSta7WHU6TaD2nE+s5yjUCrTGwvzuv0D7OmPqe2iA1QN1aVUny+b0XgRpMapx+slMwVCAePMVJLlXGXw3v6BGNmGRnAvDSYi7BuI8fr9/ddNea62i+2nkoXILrKR0oIZPH1lgUKpTH8sTKFU5ukrC9Sds7eFZp71ByULkV1ko6OnHQbUsoNV72+v1aZXf/rKIpMLGbVdbCMlC5FdZCO/0p2DUwcHCAeNZLZIOGicOjiAc9sVbUV9G92l+TTnZ5c5PuLtlCTyWmqzENlF1uo2u9pYiv54mHyxzC37+leWpfNFEtHt/41Z30Y32hvVZVY7QCULkV1kIz3p/NhFW5MQdo5KFiK7TKs96fx44SENSO0cJQsRWZPfumhvpBpN2kvVUCLSNTQgtXNUshCRruK30s5uoZKFiIg05WmyMLO7zOyMmZ01s4+u8vi9ZjZtZk9U/z5Y99gvm9lL1b9f9jJOERFZn2fVUGYWBD4N3AlcBh41s9POuecanvol59x9DesOA/8JGKdyDfnHquvOexWviIiszcuSxR3AWefcOedcHvgi8J4W1/1p4GHn3Fw1QTwM3OVRnCIi0oSXyeIgcKnu/uXqskbvM7OnzOxBMzu8wXVFRGQbdLo31FeALzjncmb2a8ADwLtaXdnMPgR8qHo3Z2bPeBCjl0aBmU4HsUGK2XvdFi8o5u3gVbxHW3mSl8niCnC47v6h6rIVzrnZurufAT5Rt+5PNqz7941v4Jy7H7gfwMwmnHPjWw16Oynm7dFtMXdbvKCYt0On4/WyGupR4ISZHTezCHAPcLr+CWa2v+7u3cDz1dsPAT9lZkNmNgT8VHWZiIh0gGclC+dc0czuo3KSDwKfdc49a2YfByacc6eBj5jZ3UARmAPura47Z2a/RyXhAHzcOTfnVawiIrI+T9ssnHNfBb7asOx36m5/DPjYGut+FvjsBt7u/s3E2GGKeXt0W8zdFi8o5u3Q0XjNbfeVTEREpOtoug8REWnKt8nCi6lCzOx2M3u6+pp/bNa+S89vNl4zu9XMvmdmz1bHm7y/bp0/NbPzdevc2q54txJz9bFS3fLTdcuPm9n3q6/5pWrnho7HbGbvrFv2hJllzey91cc6up2rz/l5M3uuehx8vm65747lteL187G8VszV5b48lteKuWPHsnPOd39UGsRfBm4AIsCTwMmG59wLfGqVdYeBc9X/Q9XbQ9XH/hF4K5Ur0H8NeLcP4r0ZOFG9fQC4CgxW7/8p8HN+28bVx5bXWP4XwD3V2/8T+Dd+ibnhGJkDEj7ZzieAx+uO0z0+P5bXitfPx/KqMfv8WF4z5k4cy34tWbR9qhCrdNPtd8494ipb9c+A93Y6Xufci865l6q3J4FrwFib4lrPVrbxqqq/bt8FPFhd9ADt28bQvph/Dviacy7dxtjW0krM/xr4dPV4xTl3rbrcr8fyqvH6/FheaxuvyifHcisxb9ux7Ndk4cVUIQert5u95nbHu8LM7qDyK+PlusV/UF3nj8ws2qZ42xFzzMwmzOyRWhEYGAEWnHPFJq/ZqZhr7gG+0LCsk9v5ZuBmM/uH6va8q8m6nT6W14p3hQ+P5fVi9uux3HQ7s43Hsl+TRSu+Ahxzzp2i8ovrgQ7H08y68VZ/Lf458CvOuXJ18ceA1wFvoVLc/A/bFy6wfsxHXWU06QeAT5rZjdsc21pa2c5v5PpBnp3eziEqVQ4/CfwC8CdmNrjNMWzEuvH69FheL2a/HsutbOdtO5b9mixamirEOZer3v0McHuTda9Ub6/5mh2KFzPrB/4W+C3n3CN161x1FTngf1MpurbLlmJ2zl2p/j9HZSqWNwOzwKCZ1cbvtHMbbznmqp8H/to5V6hbp6PbmcqvytPOuYJz7jzwIpWThC+P5XXi9e2xvF7Mfj2W14u5anuPZS8aQrb6RyWjngOO82rjz480PGd/3e2fBR5xrzb4nKfSIDhUvT1cfayxUfBnfBBvBPgG8G9Xed391f8GfBL4Lz7ZxkNAtHp7FHiJauMc8H+4vlHww36IuW7ZI8A7fbad7wIeqNuel6hUg/j1WF4rXj8fy2vF7OdjedWYO3Yst+uF2v0H/AyVTPoylV8pAB8H7q7e/s/As9WN/E3gdXXr/ipwtvr3K3XLx4Fnqq/5KaqDEjsZL/AvgQLwRN3frdXH/g54uhrz54BeP2xj4G3VuJ6s/v9Xda95A5UT2dnqly3qh5irjx2j8ust0PCand7OBvx34LlqHPf4/FheNV6fH8trxeznY3m942Lbj2WN4BYRkab82mYhIiI+omQhIiJNKVmIiEhTShYiItKUkoWIiDTl6cWPRHYDMxuhMr4AYB9QAqar9+9wlbl/RLqaus6KtJGZ/S6VWUz/a6djEWknVUOJeMjM/r2ZPVP9+/Xqspuq1yf4opk9b2Z/YWbxTscqsh4lCxGPmNmPAr9IZVK3HwM+bGZvrD58Evikc+71QBb4tc5EKdIaJQsR77wd+EvnXMY5twR8GXhH9bHz7tWJ9j5Xfa6IbylZiHRGY2OhGg/F15QsRLzzHeBnzSxuZr1UroT2nepjx83sLdXbHwC+24kARVqlZCHiEefcP1K5itmjVKaT/h/OuaerDz8P/Dszex5IAPd3JkqR1qjrrMg2M7ObgAedc7d2OhaRVqlkISIiTalkISIiTalkISIiTSlZiIhIU0oWIiLSlJKFiIg0pWQhIiJNKVmIiEhT/x9hVivDM99ZmwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f682dfb2450>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(r.top,r.S,'o',alpha=.2)\n",
    "\n",
    "plt.plot(p.top,p.S,'o')\n",
    "#plt.plot(pa.top,pa.S,'o',alpha=.5)\n",
    "plt.xlabel('Top')\n",
    "plt.ylabel('S')\n",
    "plt.xlim(.5)\n",
    "plt.ylim(.5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>name</th>\n",
       "      <th>I</th>\n",
       "      <th>testset</th>\n",
       "      <th>iteration</th>\n",
       "      <th>rmse</th>\n",
       "      <th>R</th>\n",
       "      <th>S</th>\n",
       "      <th>aucpose</th>\n",
       "      <th>aucaff</th>\n",
       "      <th>top</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1604</th>\n",
       "      <td>iteration2-g1_p0_h0</td>\n",
       "      <td>iteration2</td>\n",
       "      <td>all_pose</td>\n",
       "      <td>100k</td>\n",
       "      <td>1.678961</td>\n",
       "      <td>0.559362</td>\n",
       "      <td>0.559834</td>\n",
       "      <td>0.919870</td>\n",
       "      <td>0.771714</td>\n",
       "      <td>0.619729</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1636</th>\n",
       "      <td>iteration2-g1_p1_h0</td>\n",
       "      <td>iteration2</td>\n",
       "      <td>all_pose</td>\n",
       "      <td>100k</td>\n",
       "      <td>1.704754</td>\n",
       "      <td>0.554418</td>\n",
       "      <td>0.556157</td>\n",
       "      <td>0.920238</td>\n",
       "      <td>0.805854</td>\n",
       "      <td>0.626387</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1348</th>\n",
       "      <td>iteration2-g2_p1_h0</td>\n",
       "      <td>iteration2</td>\n",
       "      <td>all_pose</td>\n",
       "      <td>100k</td>\n",
       "      <td>1.701453</td>\n",
       "      <td>0.552951</td>\n",
       "      <td>0.552811</td>\n",
       "      <td>0.919297</td>\n",
       "      <td>0.801777</td>\n",
       "      <td>0.637731</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>68</th>\n",
       "      <td>iteration3-batch_relu_lr0.010</td>\n",
       "      <td>iteration3</td>\n",
       "      <td>all_pose</td>\n",
       "      <td>100k</td>\n",
       "      <td>1.693488</td>\n",
       "      <td>0.553803</td>\n",
       "      <td>0.551250</td>\n",
       "      <td>0.916430</td>\n",
       "      <td>0.762815</td>\n",
       "      <td>0.656720</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                               name           I   testset iteration      rmse  \\\n",
       "1604            iteration2-g1_p0_h0  iteration2  all_pose      100k  1.678961   \n",
       "1636            iteration2-g1_p1_h0  iteration2  all_pose      100k  1.704754   \n",
       "1348            iteration2-g2_p1_h0  iteration2  all_pose      100k  1.701453   \n",
       "68    iteration3-batch_relu_lr0.010  iteration3  all_pose      100k  1.693488   \n",
       "\n",
       "             R         S   aucpose    aucaff       top  \n",
       "1604  0.559362  0.559834  0.919870  0.771714  0.619729  \n",
       "1636  0.554418  0.556157  0.920238  0.805854  0.626387  \n",
       "1348  0.552951  0.552811  0.919297  0.801777  0.637731  \n",
       "68    0.553803  0.551250  0.916430  0.762815  0.656720  "
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "p.sort_values('top')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>name</th>\n",
       "      <th>I</th>\n",
       "      <th>testset</th>\n",
       "      <th>iteration</th>\n",
       "      <th>rmse</th>\n",
       "      <th>R</th>\n",
       "      <th>S</th>\n",
       "      <th>aucpose</th>\n",
       "      <th>aucaff</th>\n",
       "      <th>top</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1605</th>\n",
       "      <td>iteration2-g1_p0_h0</td>\n",
       "      <td>iteration2</td>\n",
       "      <td>all_affinity</td>\n",
       "      <td>100k</td>\n",
       "      <td>1.650597</td>\n",
       "      <td>0.575963</td>\n",
       "      <td>0.579083</td>\n",
       "      <td>0.919870</td>\n",
       "      <td>0.771714</td>\n",
       "      <td>0.561282</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1637</th>\n",
       "      <td>iteration2-g1_p1_h0</td>\n",
       "      <td>iteration2</td>\n",
       "      <td>all_affinity</td>\n",
       "      <td>100k</td>\n",
       "      <td>1.670774</td>\n",
       "      <td>0.568650</td>\n",
       "      <td>0.571575</td>\n",
       "      <td>0.920238</td>\n",
       "      <td>0.805854</td>\n",
       "      <td>0.595314</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1349</th>\n",
       "      <td>iteration2-g2_p1_h0</td>\n",
       "      <td>iteration2</td>\n",
       "      <td>all_affinity</td>\n",
       "      <td>100k</td>\n",
       "      <td>1.671064</td>\n",
       "      <td>0.566444</td>\n",
       "      <td>0.567625</td>\n",
       "      <td>0.919297</td>\n",
       "      <td>0.801777</td>\n",
       "      <td>0.606905</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1777</th>\n",
       "      <td>iteration2-g2_p2_h0</td>\n",
       "      <td>iteration2</td>\n",
       "      <td>all_affinity</td>\n",
       "      <td>100k</td>\n",
       "      <td>1.704713</td>\n",
       "      <td>0.558476</td>\n",
       "      <td>0.557435</td>\n",
       "      <td>0.922099</td>\n",
       "      <td>0.823510</td>\n",
       "      <td>0.610851</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1681</th>\n",
       "      <td>iteration2-g1_p4_h6</td>\n",
       "      <td>iteration2</td>\n",
       "      <td>all_affinity</td>\n",
       "      <td>100k</td>\n",
       "      <td>1.728620</td>\n",
       "      <td>0.551862</td>\n",
       "      <td>0.545324</td>\n",
       "      <td>0.917875</td>\n",
       "      <td>0.822993</td>\n",
       "      <td>0.612330</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1873</th>\n",
       "      <td>iteration2-g2_p4_h0</td>\n",
       "      <td>iteration2</td>\n",
       "      <td>all_affinity</td>\n",
       "      <td>100k</td>\n",
       "      <td>1.773668</td>\n",
       "      <td>0.540626</td>\n",
       "      <td>0.540151</td>\n",
       "      <td>0.917587</td>\n",
       "      <td>0.832073</td>\n",
       "      <td>0.620715</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1825</th>\n",
       "      <td>iteration2-g1_p4_h0</td>\n",
       "      <td>iteration2</td>\n",
       "      <td>all_affinity</td>\n",
       "      <td>100k</td>\n",
       "      <td>1.781914</td>\n",
       "      <td>0.528489</td>\n",
       "      <td>0.521414</td>\n",
       "      <td>0.916916</td>\n",
       "      <td>0.830906</td>\n",
       "      <td>0.630086</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     name           I       testset iteration      rmse  \\\n",
       "1605  iteration2-g1_p0_h0  iteration2  all_affinity      100k  1.650597   \n",
       "1637  iteration2-g1_p1_h0  iteration2  all_affinity      100k  1.670774   \n",
       "1349  iteration2-g2_p1_h0  iteration2  all_affinity      100k  1.671064   \n",
       "1777  iteration2-g2_p2_h0  iteration2  all_affinity      100k  1.704713   \n",
       "1681  iteration2-g1_p4_h6  iteration2  all_affinity      100k  1.728620   \n",
       "1873  iteration2-g2_p4_h0  iteration2  all_affinity      100k  1.773668   \n",
       "1825  iteration2-g1_p4_h0  iteration2  all_affinity      100k  1.781914   \n",
       "\n",
       "             R         S   aucpose    aucaff       top  \n",
       "1605  0.575963  0.579083  0.919870  0.771714  0.561282  \n",
       "1637  0.568650  0.571575  0.920238  0.805854  0.595314  \n",
       "1349  0.566444  0.567625  0.919297  0.801777  0.606905  \n",
       "1777  0.558476  0.557435  0.922099  0.823510  0.610851  \n",
       "1681  0.551862  0.545324  0.917875  0.822993  0.612330  \n",
       "1873  0.540626  0.540151  0.917587  0.832073  0.620715  \n",
       "1825  0.528489  0.521414  0.916916  0.830906  0.630086  "
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pa.sort_values('top')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f682df5bb90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f682df69850>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(12,12))\n",
    "sns.lmplot(x='top',y='R',data=results100[results100.testset == 'all_pose'],fit_reg=False,hue='I',scatter_kws={'alpha':0.6})\n",
    "plt.xlim(.5)\n",
    "plt.ylim(.5)\n",
    "plt.savefig('iterations.pdf',bbox_inches='tight')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "sdata = pd.read_csv('search_data.csv') # created with extractres.py"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f682df18f50>"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f682e03b850>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot(sdata.R)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f682e055a90>"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f682df18f90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot(sdata.top)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f682df7da90>"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f682dab1f50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot(sdata.rmse)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.717632552404"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sdata.top.max()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>top</th>\n",
       "      <th>auc</th>\n",
       "      <th>R</th>\n",
       "      <th>rmse</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.613317</td>\n",
       "      <td>0.914527</td>\n",
       "      <td>0.533481</td>\n",
       "      <td>1.834271</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.624168</td>\n",
       "      <td>0.921237</td>\n",
       "      <td>0.533601</td>\n",
       "      <td>1.845174</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.634279</td>\n",
       "      <td>0.925966</td>\n",
       "      <td>0.524212</td>\n",
       "      <td>1.749490</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.620222</td>\n",
       "      <td>0.915519</td>\n",
       "      <td>0.541174</td>\n",
       "      <td>1.724415</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.620222</td>\n",
       "      <td>0.915429</td>\n",
       "      <td>0.533995</td>\n",
       "      <td>1.773011</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.615536</td>\n",
       "      <td>0.915351</td>\n",
       "      <td>0.524260</td>\n",
       "      <td>1.769684</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0.625401</td>\n",
       "      <td>0.914728</td>\n",
       "      <td>0.547576</td>\n",
       "      <td>1.714522</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>0.637731</td>\n",
       "      <td>0.919297</td>\n",
       "      <td>0.552951</td>\n",
       "      <td>1.701453</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>0.617263</td>\n",
       "      <td>0.915837</td>\n",
       "      <td>0.538066</td>\n",
       "      <td>1.742812</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>0.614303</td>\n",
       "      <td>0.912644</td>\n",
       "      <td>0.519095</td>\n",
       "      <td>1.789310</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>0.615290</td>\n",
       "      <td>0.913756</td>\n",
       "      <td>0.527857</td>\n",
       "      <td>1.773695</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>0.621702</td>\n",
       "      <td>0.914838</td>\n",
       "      <td>0.534740</td>\n",
       "      <td>1.738086</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>0.622935</td>\n",
       "      <td>0.912770</td>\n",
       "      <td>0.553804</td>\n",
       "      <td>1.693084</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>0.616769</td>\n",
       "      <td>0.915495</td>\n",
       "      <td>0.560087</td>\n",
       "      <td>1.675865</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>0.638224</td>\n",
       "      <td>0.919930</td>\n",
       "      <td>0.519556</td>\n",
       "      <td>1.801691</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>0.618496</td>\n",
       "      <td>0.912853</td>\n",
       "      <td>0.535448</td>\n",
       "      <td>1.762682</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>0.619482</td>\n",
       "      <td>0.916119</td>\n",
       "      <td>0.537531</td>\n",
       "      <td>1.757615</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>0.619975</td>\n",
       "      <td>0.914318</td>\n",
       "      <td>0.523335</td>\n",
       "      <td>1.810894</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>0.615043</td>\n",
       "      <td>0.916189</td>\n",
       "      <td>0.523030</td>\n",
       "      <td>1.733958</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>0.618496</td>\n",
       "      <td>0.915021</td>\n",
       "      <td>0.519296</td>\n",
       "      <td>1.785828</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>0.619975</td>\n",
       "      <td>0.915519</td>\n",
       "      <td>0.541137</td>\n",
       "      <td>1.724470</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>0.629840</td>\n",
       "      <td>0.917853</td>\n",
       "      <td>0.537235</td>\n",
       "      <td>1.746628</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>0.619729</td>\n",
       "      <td>0.915976</td>\n",
       "      <td>0.533345</td>\n",
       "      <td>1.745007</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>0.631566</td>\n",
       "      <td>0.918258</td>\n",
       "      <td>0.538165</td>\n",
       "      <td>1.735999</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>0.616276</td>\n",
       "      <td>0.915368</td>\n",
       "      <td>0.539227</td>\n",
       "      <td>1.739500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>0.625401</td>\n",
       "      <td>0.914734</td>\n",
       "      <td>0.529718</td>\n",
       "      <td>1.785219</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>0.606165</td>\n",
       "      <td>0.914652</td>\n",
       "      <td>0.541611</td>\n",
       "      <td>1.744224</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>0.639457</td>\n",
       "      <td>0.916916</td>\n",
       "      <td>0.515484</td>\n",
       "      <td>1.826143</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>0.623428</td>\n",
       "      <td>0.916626</td>\n",
       "      <td>0.535801</td>\n",
       "      <td>1.761180</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>0.619236</td>\n",
       "      <td>0.916240</td>\n",
       "      <td>0.536564</td>\n",
       "      <td>1.756000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>180</th>\n",
       "      <td>0.688533</td>\n",
       "      <td>0.921800</td>\n",
       "      <td>0.545970</td>\n",
       "      <td>1.702716</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>181</th>\n",
       "      <td>0.694698</td>\n",
       "      <td>0.932548</td>\n",
       "      <td>0.563699</td>\n",
       "      <td>1.679349</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>182</th>\n",
       "      <td>0.658446</td>\n",
       "      <td>0.913884</td>\n",
       "      <td>0.546492</td>\n",
       "      <td>1.691058</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>183</th>\n",
       "      <td>0.696178</td>\n",
       "      <td>0.916064</td>\n",
       "      <td>0.545898</td>\n",
       "      <td>1.699379</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>184</th>\n",
       "      <td>0.708261</td>\n",
       "      <td>0.920734</td>\n",
       "      <td>0.576480</td>\n",
       "      <td>1.663244</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>185</th>\n",
       "      <td>0.696671</td>\n",
       "      <td>0.933664</td>\n",
       "      <td>0.586224</td>\n",
       "      <td>1.629425</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>186</th>\n",
       "      <td>0.717633</td>\n",
       "      <td>0.933611</td>\n",
       "      <td>0.579139</td>\n",
       "      <td>1.646743</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>187</th>\n",
       "      <td>0.702589</td>\n",
       "      <td>0.928618</td>\n",
       "      <td>0.536900</td>\n",
       "      <td>1.717798</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>188</th>\n",
       "      <td>0.694945</td>\n",
       "      <td>0.928164</td>\n",
       "      <td>0.575410</td>\n",
       "      <td>1.648389</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>189</th>\n",
       "      <td>0.679162</td>\n",
       "      <td>0.927451</td>\n",
       "      <td>0.542321</td>\n",
       "      <td>1.705006</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>190</th>\n",
       "      <td>0.697904</td>\n",
       "      <td>0.926328</td>\n",
       "      <td>0.549922</td>\n",
       "      <td>1.696687</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>191</th>\n",
       "      <td>0.696178</td>\n",
       "      <td>0.930033</td>\n",
       "      <td>0.583427</td>\n",
       "      <td>1.637986</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>192</th>\n",
       "      <td>0.679162</td>\n",
       "      <td>0.928100</td>\n",
       "      <td>0.554996</td>\n",
       "      <td>1.681855</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>193</th>\n",
       "      <td>0.684340</td>\n",
       "      <td>0.931879</td>\n",
       "      <td>0.585622</td>\n",
       "      <td>1.639307</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>194</th>\n",
       "      <td>0.684834</td>\n",
       "      <td>0.925412</td>\n",
       "      <td>0.557428</td>\n",
       "      <td>1.682890</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>195</th>\n",
       "      <td>0.684340</td>\n",
       "      <td>0.923530</td>\n",
       "      <td>0.541589</td>\n",
       "      <td>1.718924</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>196</th>\n",
       "      <td>0.700370</td>\n",
       "      <td>0.916046</td>\n",
       "      <td>0.531468</td>\n",
       "      <td>1.732090</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>197</th>\n",
       "      <td>0.672503</td>\n",
       "      <td>0.926666</td>\n",
       "      <td>0.562508</td>\n",
       "      <td>1.676187</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>198</th>\n",
       "      <td>0.688286</td>\n",
       "      <td>0.917091</td>\n",
       "      <td>0.537483</td>\n",
       "      <td>1.722038</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>199</th>\n",
       "      <td>0.702589</td>\n",
       "      <td>0.928618</td>\n",
       "      <td>0.536900</td>\n",
       "      <td>1.717798</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>200</th>\n",
       "      <td>0.679162</td>\n",
       "      <td>0.927451</td>\n",
       "      <td>0.542321</td>\n",
       "      <td>1.705006</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>201</th>\n",
       "      <td>0.686313</td>\n",
       "      <td>0.924801</td>\n",
       "      <td>0.553252</td>\n",
       "      <td>1.685514</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>202</th>\n",
       "      <td>0.677682</td>\n",
       "      <td>0.924081</td>\n",
       "      <td>0.567986</td>\n",
       "      <td>1.664400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>203</th>\n",
       "      <td>0.674723</td>\n",
       "      <td>0.919748</td>\n",
       "      <td>0.559074</td>\n",
       "      <td>1.679121</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>204</th>\n",
       "      <td>0.680641</td>\n",
       "      <td>0.928221</td>\n",
       "      <td>0.588830</td>\n",
       "      <td>1.630622</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>205</th>\n",
       "      <td>0.694698</td>\n",
       "      <td>0.932548</td>\n",
       "      <td>0.563699</td>\n",
       "      <td>1.679349</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>206</th>\n",
       "      <td>0.708261</td>\n",
       "      <td>0.920734</td>\n",
       "      <td>0.576480</td>\n",
       "      <td>1.663244</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>207</th>\n",
       "      <td>0.717633</td>\n",
       "      <td>0.933611</td>\n",
       "      <td>0.579139</td>\n",
       "      <td>1.646743</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>208</th>\n",
       "      <td>0.702589</td>\n",
       "      <td>0.928618</td>\n",
       "      <td>0.536900</td>\n",
       "      <td>1.717798</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>209</th>\n",
       "      <td>0.697904</td>\n",
       "      <td>0.926328</td>\n",
       "      <td>0.549922</td>\n",
       "      <td>1.696687</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>210 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          top       auc         R      rmse\n",
       "0    0.613317  0.914527  0.533481  1.834271\n",
       "1    0.624168  0.921237  0.533601  1.845174\n",
       "2    0.634279  0.925966  0.524212  1.749490\n",
       "3    0.620222  0.915519  0.541174  1.724415\n",
       "4    0.620222  0.915429  0.533995  1.773011\n",
       "5    0.615536  0.915351  0.524260  1.769684\n",
       "6    0.625401  0.914728  0.547576  1.714522\n",
       "7    0.637731  0.919297  0.552951  1.701453\n",
       "8    0.617263  0.915837  0.538066  1.742812\n",
       "9    0.614303  0.912644  0.519095  1.789310\n",
       "10   0.615290  0.913756  0.527857  1.773695\n",
       "11   0.621702  0.914838  0.534740  1.738086\n",
       "12   0.622935  0.912770  0.553804  1.693084\n",
       "13   0.616769  0.915495  0.560087  1.675865\n",
       "14   0.638224  0.919930  0.519556  1.801691\n",
       "15   0.618496  0.912853  0.535448  1.762682\n",
       "16   0.619482  0.916119  0.537531  1.757615\n",
       "17   0.619975  0.914318  0.523335  1.810894\n",
       "18   0.615043  0.916189  0.523030  1.733958\n",
       "19   0.618496  0.915021  0.519296  1.785828\n",
       "20   0.619975  0.915519  0.541137  1.724470\n",
       "21   0.629840  0.917853  0.537235  1.746628\n",
       "22   0.619729  0.915976  0.533345  1.745007\n",
       "23   0.631566  0.918258  0.538165  1.735999\n",
       "24   0.616276  0.915368  0.539227  1.739500\n",
       "25   0.625401  0.914734  0.529718  1.785219\n",
       "26   0.606165  0.914652  0.541611  1.744224\n",
       "27   0.639457  0.916916  0.515484  1.826143\n",
       "28   0.623428  0.916626  0.535801  1.761180\n",
       "29   0.619236  0.916240  0.536564  1.756000\n",
       "..        ...       ...       ...       ...\n",
       "180  0.688533  0.921800  0.545970  1.702716\n",
       "181  0.694698  0.932548  0.563699  1.679349\n",
       "182  0.658446  0.913884  0.546492  1.691058\n",
       "183  0.696178  0.916064  0.545898  1.699379\n",
       "184  0.708261  0.920734  0.576480  1.663244\n",
       "185  0.696671  0.933664  0.586224  1.629425\n",
       "186  0.717633  0.933611  0.579139  1.646743\n",
       "187  0.702589  0.928618  0.536900  1.717798\n",
       "188  0.694945  0.928164  0.575410  1.648389\n",
       "189  0.679162  0.927451  0.542321  1.705006\n",
       "190  0.697904  0.926328  0.549922  1.696687\n",
       "191  0.696178  0.930033  0.583427  1.637986\n",
       "192  0.679162  0.928100  0.554996  1.681855\n",
       "193  0.684340  0.931879  0.585622  1.639307\n",
       "194  0.684834  0.925412  0.557428  1.682890\n",
       "195  0.684340  0.923530  0.541589  1.718924\n",
       "196  0.700370  0.916046  0.531468  1.732090\n",
       "197  0.672503  0.926666  0.562508  1.676187\n",
       "198  0.688286  0.917091  0.537483  1.722038\n",
       "199  0.702589  0.928618  0.536900  1.717798\n",
       "200  0.679162  0.927451  0.542321  1.705006\n",
       "201  0.686313  0.924801  0.553252  1.685514\n",
       "202  0.677682  0.924081  0.567986  1.664400\n",
       "203  0.674723  0.919748  0.559074  1.679121\n",
       "204  0.680641  0.928221  0.588830  1.630622\n",
       "205  0.694698  0.932548  0.563699  1.679349\n",
       "206  0.708261  0.920734  0.576480  1.663244\n",
       "207  0.717633  0.933611  0.579139  1.646743\n",
       "208  0.702589  0.928618  0.536900  1.717798\n",
       "209  0.697904  0.926328  0.549922  1.696687\n",
       "\n",
       "[210 rows x 4 columns]"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sdata.loc[:,['top','auc','R','rmse']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1, 2)"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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IACBwBAEABI4gAIDAEQQAEDiCAAACRxAAQOAIAgAIHEEAAIEjCAAgcAQBAASOIACAwBEEABA4ggAAAkcQAEDgCAIACFzFIDCzW81sj5k9WmG9d5jZfjO7KL3yAACNVs0VwTpJy8qtYGbdkr4g6X9SqAkA0EQVg8DdH5S0t8JqfyPpLkl70igKANA8dfcRmFm/pPdJ+koV6640syEzGxoZGan3pQEAKUijs/gmSVe5+4FKK7r7WncfcPeBvr6+FF4aAFCvaSlsY0DSN81MkmZKOt/M9rv7YArbBgA0WN1B4O4nF342s3WSvk8IAEDnqBgEZrZe0tmSZprZLknXScpIkrvf0tDqAAANVzEI3P2Sajfm7h+pqxoAQNNxZzEABI4gAIDAEQQAEDiCAAACRxAAQOAIAgAIHEEAAIEjCAAgcAQBAASOIACAwBEEABA4ggAAAkcQAEDgCAIACBxBAACBIwgAIHAEAQAEjiAAgMARBAAQOIIAAAJHEABA4AgCAAgcQQAAgSMIACBwBAEABI4gAIDAEQQAEDiCAAACRxAAQOAIAgAIHEEAAIEjCAAgcAQBAASOIACAwFUMAjO71cz2mNmjCY9fambbzGy7mf3EzM5Mv0wAQKNUc0WwTtKyMo8/K+kP3X2+pOslrU2hLgBAk0yrtIK7P2hmc8o8/pPYr5slnVh/WQCAZkm7j+Cjku5JetDMVprZkJkNjYyMpPzSAIDJSC0IzOwc5YPgqqR13H2tuw+4+0BfX19aLw0AqEPFpqFqmNnbJH1V0nnu/mIa2wQANEfdVwRmNlvSBkkfcven6i8JANBMFa8IzGy9pLMlzTSzXZKuk5SRJHe/RdLfS3qTpC+bmSTtd/eBRhUMAEhXNaOGLqnw+MckfSy1igAATcWdxQAQOIIAAAJHEABA4AgCAAgcQQAAgSMIACBwBAEABI4gAIDAEQQAEDiCAAACRxAAQOAIAgAIHEEAAIEjCAAgcAQBAASOIACAwBEEABA4ggAAAkcQAEDgCAIACBxBAACBIwgAIHAEAQAEjiAAgMARBAAQOIIAAAJHEABA4AgCAAgcQQAAgSMIACBwBAEABI4gAIDAEQQAEDiCAAACRxAAQOAqBoGZ3Wpme8zs0YTHzcy+ZGY7zWybmb09/TIBAI1SzRXBOknLyjx+nqTToq+Vkr5Sf1kAgGapGATu/qCkvWVWea+k2zxvs6ReMzshrQIBAI01LYVt9Et6Pvb7rmjZC8UrmtlK5a8aJOm1pOamNjNT0q9aXUQVqDNdnVBnJ9QoUWfa5qa9wTSCoGruvlbSWkkysyF3H2jm608GdaaLOtPTCTVK1Jk2MxtKe5tpjBrKSjop9vuJ0TIAQAdIIwg2Sro8Gj20WNLL7n5YsxAAoD1VbBoys/WSzpY008x2SbpOUkaS3P0WST+QdL6knZL2SbqiytdeO4l6W4E600Wd6emEGiXqTFvqdZq7p71NAEAH4c5iAAgcQQAAgUstCMxsmZntiKaauLrMeivMzM1sILbsmuh5O8xsaa3bbHSNZvbHZrbFzLZH38+NrftAtM2t0dfxLaxzjpmNxWq5Jbbuoqj+ndGUINbCOi+N1bjVzA6Y2YLosabvTzP7iJmNxF7zY7HHPmxmP4++Phxb3vT9mVSnmS0ws5+a2WPRNC8Xx56zzsyejT1nQavqjB4bjy3fGFt+spk9FG3zTjM7ohU1mtk5Re/NV81sefRY0/dltM77zezx6N/3G7Hl6b033b3uL0ndkp6WdIqkIyQ9IuktJdY7RtKDkjZLGoiWvSVa/0hJJ0fb6a52m02qcaGkWdHPb5WUja3/QGG9NtiXcyQ9mrDdn0laLMkk3SPpvFbVWfT4fElPt3J/SvqIpJtLPPc4Sc9E32dEP89o1f4sU+fpkk6Lfp6l/M2cvdHv6yRd1A77M3rstwnLvyXpA9HPt0j6q1bVWPTvv1fS9Bbuy9MkDcfed8c34r2Z1hXBOyXtdPdn3P11Sd9UfuqJYtdL+oKkV2PL3ivpm+7+mrs/q/zoo3fWsM2G1+juw+6+O/r1MUk9ZnZkHbU0pM4klp/y41h33+z5d8ptkpa3SZ2XRM9tlHreR0sl3evue939JUn3SlrW4v15GHd/yt1/Hv28W9IeSX111pN6nUmiM9ZzJX0nWvR11bc/06rxIkn3uPu+Omopp5o6Py7p36L3n9x9T7Q81fdmWkGQNM3EQZaflfQkd7+7yudW3GYTa4xbIelhd38ttuxr0aXi51JoIqi3zpPNbNjMfmRmfxDb5q5y22xBnQUXS1pftKyp+zOyImpW+Y6ZFW6QLPfebPr+LFPnQWb2TuXPLp+OLf6n6Dn/ksIJTL11HmVmQ2a2udDkIulNkkbdfX+FbTarxoIP6PD3ZrP35emSTjezH0f7bFmF507qvdmUzmIz65L0RUmfbsbrTUY1NZrZGcqf3f5lbPGl7j5f0h9EXx9qYZ0vSJrt7gslfUrSN8zs2EbWk6TK/XmWpH3uHp9zqqn7M/I9SXPc/W3Kn1l9vQmvORll64zOBv9T0hXufiBafI2keZLeoXwzwlUtrvN3PT+Nwwcl3WRmpzahnlKq2ZfzJW2KLW7FvpymfPPQ2cpfPf+7mfWm/SJpBUGlaSaOUb5t/QEz+4Xy7VcbLd95mPTctKeuqKdGmdmJkr4r6XJ3P3i25e7Z6PtvJH1D+cu9eky6zqh57cWoni3KnxWeHj3/xDLbbGqdsXUOO+Nqwf6Uu78Yu8L7qqRFFZ7biv1Zrk5FgX+3pM96fhbgwnNe8LzXJH1Nrd2f8X/fZ5TvD1oo6UXlZy0u3ODa6P/rZWuMvF/Sd909F3tO0/el8mf0G909FzWdP6V8MKT73kyp02Oa8p0VJ+tQp8cZZdZ/QIc6OM/QxM7iZ5TvRKlpmw2usTda/8IS25wZ/ZxRvo3zEy3cl32SuqOfT4neAMd56Q6k81tVZ/R7V1TfKa3en5JOiP38Pkmbo5+Pk/Ss8p1xM6KfW7Y/y9R5hKQfSrqyxHZPiL6bpJskrW5hnTMkHRn9PFPSzxV1jkr6tiZ2Fv91K2qMLdss6Zw22JfLJH09ts+eV74pLdX35qT/iBJ/1PnKp9XTyp+VSNI/SvqzEus+oIkHhc9Gz9uhWA93qW22okZJ10p6RdLW2Nfxko6WtEXSNuU7kf9V0YG4RXWuiOrYKulhSRfE1huQ9Gi0zZsV3VXewn/zs0v852vJ/pR0Y/R6j0i6X9K82HP/QvkBDDuVb3Jp2f5MqlPSZZJyRe/PBdFj90naHtV6u6Q3tLDOd0e1PBJ9/2hsm6cofwDbqXwoHNnCf/M5yp+kdBVtsxX70pRvYn08eu0PNOK9yRQTABA47iwGgMARBAAQOIIAAAJHEABA4AgCAAgcQQAAgSMIACBw/w915x0kBdWm8gAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f682df73810>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(sdata.R,sdata.rmse,'o')\n",
    "plt.xlim(.4,.6)\n",
    "plt.ylim(1,2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>name</th>\n",
       "      <th>I</th>\n",
       "      <th>testset</th>\n",
       "      <th>iteration</th>\n",
       "      <th>rmse</th>\n",
       "      <th>R</th>\n",
       "      <th>S</th>\n",
       "      <th>aucpose</th>\n",
       "      <th>aucaff</th>\n",
       "      <th>top</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2497</th>\n",
       "      <td>iteration5-64-32-32_3_0_2</td>\n",
       "      <td>iteration5</td>\n",
       "      <td>all_affinity</td>\n",
       "      <td>100k</td>\n",
       "      <td>1.685173</td>\n",
       "      <td>0.556186</td>\n",
       "      <td>0.551134</td>\n",
       "      <td>0.871853</td>\n",
       "      <td>0.721267</td>\n",
       "      <td>0.500863</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2496</th>\n",
       "      <td>iteration5-64-32-32_3_0_2</td>\n",
       "      <td>iteration5</td>\n",
       "      <td>all_pose</td>\n",
       "      <td>100k</td>\n",
       "      <td>1.681208</td>\n",
       "      <td>0.554549</td>\n",
       "      <td>0.548422</td>\n",
       "      <td>0.871853</td>\n",
       "      <td>0.721267</td>\n",
       "      <td>0.559309</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2225</th>\n",
       "      <td>iteration5-32-32-32_7_0_2</td>\n",
       "      <td>iteration5</td>\n",
       "      <td>all_affinity</td>\n",
       "      <td>100k</td>\n",
       "      <td>1.689232</td>\n",
       "      <td>0.546925</td>\n",
       "      <td>0.546270</td>\n",
       "      <td>0.904420</td>\n",
       "      <td>0.750968</td>\n",
       "      <td>0.556104</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2224</th>\n",
       "      <td>iteration5-32-32-32_7_0_2</td>\n",
       "      <td>iteration5</td>\n",
       "      <td>all_pose</td>\n",
       "      <td>100k</td>\n",
       "      <td>1.697964</td>\n",
       "      <td>0.542823</td>\n",
       "      <td>0.540535</td>\n",
       "      <td>0.904420</td>\n",
       "      <td>0.750968</td>\n",
       "      <td>0.619236</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2256</th>\n",
       "      <td>iteration5-32-16-16_3_0_2</td>\n",
       "      <td>iteration5</td>\n",
       "      <td>all_pose</td>\n",
       "      <td>100k</td>\n",
       "      <td>1.699218</td>\n",
       "      <td>0.539285</td>\n",
       "      <td>0.533350</td>\n",
       "      <td>0.877813</td>\n",
       "      <td>0.717541</td>\n",
       "      <td>0.543773</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2257</th>\n",
       "      <td>iteration5-32-16-16_3_0_2</td>\n",
       "      <td>iteration5</td>\n",
       "      <td>all_affinity</td>\n",
       "      <td>100k</td>\n",
       "      <td>1.704539</td>\n",
       "      <td>0.538173</td>\n",
       "      <td>0.532238</td>\n",
       "      <td>0.877813</td>\n",
       "      <td>0.717541</td>\n",
       "      <td>0.485080</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2513</th>\n",
       "      <td>iteration5-64-32-32_7_0_2</td>\n",
       "      <td>iteration5</td>\n",
       "      <td>all_affinity</td>\n",
       "      <td>100k</td>\n",
       "      <td>1.713270</td>\n",
       "      <td>0.532251</td>\n",
       "      <td>0.528519</td>\n",
       "      <td>0.902869</td>\n",
       "      <td>0.738239</td>\n",
       "      <td>0.517633</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2241</th>\n",
       "      <td>iteration5-64-32-16_3_0_2</td>\n",
       "      <td>iteration5</td>\n",
       "      <td>all_affinity</td>\n",
       "      <td>100k</td>\n",
       "      <td>1.706995</td>\n",
       "      <td>0.531288</td>\n",
       "      <td>0.532508</td>\n",
       "      <td>0.874228</td>\n",
       "      <td>0.710385</td>\n",
       "      <td>0.492478</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2240</th>\n",
       "      <td>iteration5-64-32-16_3_0_2</td>\n",
       "      <td>iteration5</td>\n",
       "      <td>all_pose</td>\n",
       "      <td>100k</td>\n",
       "      <td>1.716807</td>\n",
       "      <td>0.527506</td>\n",
       "      <td>0.528589</td>\n",
       "      <td>0.874228</td>\n",
       "      <td>0.710385</td>\n",
       "      <td>0.561529</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2289</th>\n",
       "      <td>iteration5-32-32-32_3_0_2</td>\n",
       "      <td>iteration5</td>\n",
       "      <td>all_affinity</td>\n",
       "      <td>100k</td>\n",
       "      <td>1.718968</td>\n",
       "      <td>0.526965</td>\n",
       "      <td>0.523700</td>\n",
       "      <td>0.884796</td>\n",
       "      <td>0.729079</td>\n",
       "      <td>0.490752</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2288</th>\n",
       "      <td>iteration5-32-32-32_3_0_2</td>\n",
       "      <td>iteration5</td>\n",
       "      <td>all_pose</td>\n",
       "      <td>100k</td>\n",
       "      <td>1.712407</td>\n",
       "      <td>0.526708</td>\n",
       "      <td>0.522618</td>\n",
       "      <td>0.884796</td>\n",
       "      <td>0.729079</td>\n",
       "      <td>0.553637</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2481</th>\n",
       "      <td>iteration5-32-16-16_7_0_2</td>\n",
       "      <td>iteration5</td>\n",
       "      <td>all_affinity</td>\n",
       "      <td>100k</td>\n",
       "      <td>1.728476</td>\n",
       "      <td>0.522732</td>\n",
       "      <td>0.516890</td>\n",
       "      <td>0.901181</td>\n",
       "      <td>0.747794</td>\n",
       "      <td>0.518619</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2480</th>\n",
       "      <td>iteration5-32-16-16_7_0_2</td>\n",
       "      <td>iteration5</td>\n",
       "      <td>all_pose</td>\n",
       "      <td>100k</td>\n",
       "      <td>1.733871</td>\n",
       "      <td>0.521145</td>\n",
       "      <td>0.514545</td>\n",
       "      <td>0.901181</td>\n",
       "      <td>0.747794</td>\n",
       "      <td>0.617016</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2512</th>\n",
       "      <td>iteration5-64-32-32_7_0_2</td>\n",
       "      <td>iteration5</td>\n",
       "      <td>all_pose</td>\n",
       "      <td>100k</td>\n",
       "      <td>1.742788</td>\n",
       "      <td>0.519979</td>\n",
       "      <td>0.512631</td>\n",
       "      <td>0.902869</td>\n",
       "      <td>0.738239</td>\n",
       "      <td>0.613317</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2369</th>\n",
       "      <td>iteration5-64-32-16_7_0_2</td>\n",
       "      <td>iteration5</td>\n",
       "      <td>all_affinity</td>\n",
       "      <td>100k</td>\n",
       "      <td>1.743695</td>\n",
       "      <td>0.510761</td>\n",
       "      <td>0.511302</td>\n",
       "      <td>0.907768</td>\n",
       "      <td>0.754949</td>\n",
       "      <td>0.526757</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2368</th>\n",
       "      <td>iteration5-64-32-16_7_0_2</td>\n",
       "      <td>iteration5</td>\n",
       "      <td>all_pose</td>\n",
       "      <td>100k</td>\n",
       "      <td>1.763878</td>\n",
       "      <td>0.499588</td>\n",
       "      <td>0.497190</td>\n",
       "      <td>0.907768</td>\n",
       "      <td>0.754949</td>\n",
       "      <td>0.613810</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                           name           I       testset iteration      rmse  \\\n",
       "2497  iteration5-64-32-32_3_0_2  iteration5  all_affinity      100k  1.685173   \n",
       "2496  iteration5-64-32-32_3_0_2  iteration5      all_pose      100k  1.681208   \n",
       "2225  iteration5-32-32-32_7_0_2  iteration5  all_affinity      100k  1.689232   \n",
       "2224  iteration5-32-32-32_7_0_2  iteration5      all_pose      100k  1.697964   \n",
       "2256  iteration5-32-16-16_3_0_2  iteration5      all_pose      100k  1.699218   \n",
       "2257  iteration5-32-16-16_3_0_2  iteration5  all_affinity      100k  1.704539   \n",
       "2513  iteration5-64-32-32_7_0_2  iteration5  all_affinity      100k  1.713270   \n",
       "2241  iteration5-64-32-16_3_0_2  iteration5  all_affinity      100k  1.706995   \n",
       "2240  iteration5-64-32-16_3_0_2  iteration5      all_pose      100k  1.716807   \n",
       "2289  iteration5-32-32-32_3_0_2  iteration5  all_affinity      100k  1.718968   \n",
       "2288  iteration5-32-32-32_3_0_2  iteration5      all_pose      100k  1.712407   \n",
       "2481  iteration5-32-16-16_7_0_2  iteration5  all_affinity      100k  1.728476   \n",
       "2480  iteration5-32-16-16_7_0_2  iteration5      all_pose      100k  1.733871   \n",
       "2512  iteration5-64-32-32_7_0_2  iteration5      all_pose      100k  1.742788   \n",
       "2369  iteration5-64-32-16_7_0_2  iteration5  all_affinity      100k  1.743695   \n",
       "2368  iteration5-64-32-16_7_0_2  iteration5      all_pose      100k  1.763878   \n",
       "\n",
       "             R         S   aucpose    aucaff       top  \n",
       "2497  0.556186  0.551134  0.871853  0.721267  0.500863  \n",
       "2496  0.554549  0.548422  0.871853  0.721267  0.559309  \n",
       "2225  0.546925  0.546270  0.904420  0.750968  0.556104  \n",
       "2224  0.542823  0.540535  0.904420  0.750968  0.619236  \n",
       "2256  0.539285  0.533350  0.877813  0.717541  0.543773  \n",
       "2257  0.538173  0.532238  0.877813  0.717541  0.485080  \n",
       "2513  0.532251  0.528519  0.902869  0.738239  0.517633  \n",
       "2241  0.531288  0.532508  0.874228  0.710385  0.492478  \n",
       "2240  0.527506  0.528589  0.874228  0.710385  0.561529  \n",
       "2289  0.526965  0.523700  0.884796  0.729079  0.490752  \n",
       "2288  0.526708  0.522618  0.884796  0.729079  0.553637  \n",
       "2481  0.522732  0.516890  0.901181  0.747794  0.518619  \n",
       "2480  0.521145  0.514545  0.901181  0.747794  0.617016  \n",
       "2512  0.519979  0.512631  0.902869  0.738239  0.613317  \n",
       "2369  0.510761  0.511302  0.907768  0.754949  0.526757  \n",
       "2368  0.499588  0.497190  0.907768  0.754949  0.613810  "
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "results100[results100.name.str.startswith('iteration5')&results100.name.str.endswith('0_2') ].sort_values(by='R',ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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